A Multi-Task Learning Framework for Head Pose Estimation under Target Motion

2016 ◽  
Vol 38 (6) ◽  
pp. 1070-1083 ◽  
Author(s):  
Yan Yan ◽  
Elisa Ricci ◽  
Ramanathan Subramanian ◽  
Gaowen Liu ◽  
Oswald Lanz ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Target Motion ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Learning Framework ◽  
Task Learning

Visual Focus of Attention and Spontaneous Smile Recognition Based on Continuous Head Pose Estimation by Cascaded Multi-Task Learning

2019 ◽  
Vol 33 (07) ◽  
pp. 1940006
Author(s):  
Yuanyuan Liu ◽  
Xingmei Li ◽  
Fang Fang ◽  
Fayong Zhang ◽  
Jingying Chen ◽  
...  
Keyword(s):  
Pose Estimation ◽  
Classroom Environment ◽  
Recognition System ◽  
Focus Of Attention ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Training Time ◽  
Behavior Understanding ◽  
Task Learning ◽  
Smile Recognition

Multi-person Visual focus of attention (M-VFOA) and spontaneous smile (SS) recognition are important for persons’ behavior understanding and analysis in class. Recently, promising results have been reported using special hardware in constrained environment. However, M-VFOA and SS remain challenging problems in natural and crowd classroom environment, e.g. various poses, occlusion, expressions, illumination and poor image quality, etc. In this study, a robust and un-invasive M-VFOA and SS recognition system has been developed based on continuous head pose estimation in the natural classroom. A novel cascaded multi-task Hough forest (CM-HF) combined with weighted Hough voting and multi-task learning is proposed for continuous head pose estimation, tip of the nose location and SS recognition, which improves accuracies of recognition and reduces the training time. Then, M-VFOA can be recognized based on estimated head poses, environmental cues and prior states in the natural classroom. Meanwhile, SS is classified using CM-HF with local cascaded mouth-eyes areas normalized by the estimated head poses. The method is rigorously evaluated for continuous head pose estimation, multi-person VFOA recognition, and SS recognition on some public available datasets and real-class video sequences. Experimental results show that our method reduces training time greatly and outperforms the state-of-the-art methods for both performance and robustness with an average accuracy of 83.5% on head pose estimation, 67.8% on M-VFOA recognition and 97.1% on SS recognition in challenging environments.

The More, the Merrier? A Study on In-Car IR-based Head Pose Estimation

2020 ◽  
Author(s):  
Ahmet Firintepe ◽  
Mohamed Selim ◽  
Alain Pagani ◽  
Didier Stricker
Keyword(s):  
Pose Estimation ◽  
Head Pose Estimation ◽  
Head Pose

Early Warning Pedestrian Crossing Intention From Its Head Gesture using Head Pose Estimation

2021 ◽  
Author(s):  
Muhammad Ilham Perdana ◽  
Wiwik Anggraeni ◽  
Hanugra Aulia Sidharta ◽  
Eko Mulyanto Yuniarno ◽  
Mauridhi Hery Purnomo
Keyword(s):  
Pose Estimation ◽  
Early Warning ◽  
Head Pose Estimation ◽  
Head Pose ◽  
Pedestrian Crossing
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